Keywords: Manipulation in space, Orbital servicing and debris removal
Abstract: This paper presents a case study on a nonlinear H∞ optimal control strategy for a free-floating space manipulator system equipped with reaction wheels. To this end, the system dynamics are reformulated in a reduced form with respect to the active joints, while preserving the structural properties of Euler–Lagrange systems. This structural consistency allows for the direct application of the nonlinear H∞ controller. Furthermore, modeling uncertainties in the inertia matrix are incorporated into the kinematic formulation to ensure accurate mapping between the task-space velocity and the active joint velocity. Simulation studies under load uncertainties at the end-effector and in the presence of active joint frictions confirm the effectiveness of the proposed controller, demonstrating superior performance over a conventional inverse dynamics-based controller in both base satellite attitude regulation and 6-degrees-of-freedom trajectory tracking tasks.

Keywords: Manipulation in space, Intelligent and autonomous space robotics systems, Orbital servicing and debris removal
Abstract: End-effector control of flexible space manipulators is particularly difficult when dealing with large payloads of unknown mass. This is a relevant problem in active debris removal missions which involve grasping non-cooperative targets. This paper introduces a simultaneous parameter and state estimation algorithm that addresses the challenge of handling uncertainties from both the elastic deformation of the manipulator links as well as the unknown payload mass properties. The payload parameters are estimated using an indirect model reference adaptive control scheme, and the elastic coordinates are estimated using a Kalman filter, as well as a quasi-static closed-form estimation formula. Both the parameter and state estimates are used to improve controller performance - the estimated elastic coordinates are used to construct a modified control output called the mu-tip rate which is used for task-space feedback control, while the estimated payload parameters are used to improve both the feedforward control as well as the model used in the state estimator. It is shown that the algorithm is able to converge to an accurate estimate of the payload mass and successfully stabilize the elastic deflections at the tip.

Keywords: Orbital servicing and debris removal, Manipulation in space, Intelligent and autonomous space robotics systems
Abstract: Knowing the inertia parameters of a grasped object is crucial for dynamics-aware manipulation, especially in space robotics with free-floating bases. This work addresses the problem of estimating the inertia parameters of an unknown target object during manipulation. We apply and extend an existing online identification method by incorporating momentum conservation, enabling its use for the floating-base robots. The proposed method is validated through numerical simulations, and the estimated parameters are compared with ground-truth values. Results demonstrate accurate identification in the scenarios, highlighting the method’s applicability to on-orbit servicing and other space missions.

Keywords: Manipulation in space, Intelligent and autonomous space robotics systems, Orbital servicing and debris removal
Abstract: The growing need for space debris mitigation and satellite life extension has intensified interest in Space Manipulator Systems (SMS) for Active Debris Removal (ADR) and On-Orbit Servicing (OOS). Among the most critical challenges is the capture of uncooperative, tumbling targets. This work builds on a robust control architecture based on Nonlinear Dynamic Inversion (NDI) and Nonlinear Disturbance Observation (NDO), previously applied in On-Orbit Assembly, and adapts it to the unique dynamics of target capture. We focus on two key challenges: managing uncertainties due to incomplete target knowledge and handling the momentum transfer during post-capture stabilization. To ensure robustness throughout both pre- and post-capture phases, we propose a joint synthesis of control and observer gains using Linear Matrix Inequalities (LMI). The proposed framework is validated in high-fidelity simulations across a range of target inertias and uncertainty sources, demonstrating its effectiveness and feasibility for future ADR and OOS missions.

Keywords: Manipulation in space, Intelligent and autonomous space robotics systems
Abstract: This study focuses on motion planning of a free-floating space robot with a single manipulator arm with two degrees of freedom. We develop a concise method of controlling the attitude of the space robot using arm motions under conditions of zero total angular momentum with no external torque. Our method first investigates the gauge fields in shape space of the space robot, which are responsible for the coupling between arm motions and attitude motions. We then identify three fundamental cyclic motion patterns of the arm that can efficiently change the overall attitude of the space robot about three independent axes under conditions of zero total angular momentum with no external torque. Combining, scaling, and repeating these fundamental cyclic motion patterns based on quaternion formalism and Newton's method, our method achieves a general, large-angle, and precise attitude control of the space robot.

Keywords: Multi-robot cooperation/collaboration, Manipulation in space
Abstract: Dynamic simulation plays a critical role in the design and testing of space robotic systems. However, it becomes challenging when dealing with complex systems or tasks involving interactions among multiple subsystems. In such scenarios, improving simulation efficiency is essential. Techniques like model order reduction and co-simulation can be effective in addressing these challenges. The reduced interface model (RIM) is a recently introduced model order reduction technique that captures the dynamics of the full system within a reduced space of motion of interest. RIM can enhance simulation performance, particularly in model-based co-simulation, making it well-suited for space robotics applications that involve subsystem interactions. When applying RIM to robotic arms with flexible components, special considerations must be taken during its formulation. This paper presents a systematic derivation of the flexible RIM and demonstrates its effectiveness in space robotic interaction tasks.

Keywords: Intelligent and autonomous space robotics systems, Multi-robot cooperation/collaboration, Planetary exploration
Abstract: Spherical robots are well-suited for lunar crater exploration, offering a sealed architecture that protects internal avionics from tip-over events and abrasive lunar regolith. However, this enclosed design limits terrain perception, particularly the ability to sense local ground inclination, which is critical for descending steep craters. The challenge is even greater in inflatable variants, where routing sensors through the pressurized shell is impractical. This work introduces Remora, a novel, compact, externally mounted dual-LiDAR module that enables real-time slope estimation without breaching the shell. Remora measures both forward and lateral terrain angles and transmits this information to the robot’s onboard systems for slope-aware navigation. Implemented on the 0.61 m diameter, 40 kg RoboBall II, redesigned Gen-2 hubcaps reduced shell mass by 45% while supporting the sensing hardware. Static ramp experiments demonstrated sub-2° standard deviation in slope estimation, while dynamic tests confirmed reliable lateral performance but with degraded forward-slope accuracy during motion. This is attributed to scan distortion and mount compliance. The results validate Remora as a compact, practical solution for real-time terrain-inclination sensing in sealed spherical robots descending lunar craters.

Keywords: Intelligent and autonomous space robotics systems, Planetary exploration, Mission planning
Abstract: Autonomous navigation on unstructured and uneven terrain remains a critical challenge for planetary rovers, requiring both safe path planning and adaptive speed control. Traditional occupancy or elevation maps often fail to capture the full complexity of terrain hazards and rover mobility constraints. We present a global planning framework that integrates traversability analysis with velocity profile estimation. Local terrain features such as slope, step height, and roughness are extracted from elevation data and normalized by rover-specific parameters including maximum admissible slope and ground clearance. These form a costmap used in graph-based global planning, while the planner simultaneously adjusts target speeds to safely negotiate terrain hazards. Our method produces dynamically feasible trajectories that improve autonomous exploration capabilities in challenging planetary environments. Experimental results demonstrate that our approach improves safety by 44%, reduces maximum terrain hazard by over 10%, and decreases traversal time by nearly 10% compared to state-of-the-art planners.

Keywords: Planetary exploration, Intelligent and autonomous space robotics systems, Augmented/Virtual/Extended reality
Abstract: Autonomous navigation in unstructured environments is essential for field and planetary robotics, where robots must efficiently reach goals while avoiding obstacles under uncertain conditions. Conventional algorithmic approaches often require extensive environment-specific tuning, limiting scalability to new domains. Deep Reinforcement Learning (DRL) provides a data-driven alternative, allowing robots to acquire navigation strategies through direct interactions with their environment. This work investigates the feasibility of DRL policy generalization across visually and topographically distinct simulated domains, where policies are trained in terrestrial settings and validated in a zero-shot manner in extraterrestrial environments. A 3D simulation of an agricultural rover is developed and trained using Proximal Policy Optimization (PPO) to achieve goal-directed navigation and obstacle avoidance in farmland settings. The learned policy is then evaluated in a lunar-like simulated environment to assess transfer performance. The results indicate that policies trained under terrestrial conditions retain a high level of effectiveness, achieving close to 50% success in lunar simulations without the need for additional training and fine-tuning. This underscores the potential of cross-domain DRL-based policy transfer as a promising approach to developing adaptable and efficient autonomous navigation for future planetary exploration missions, with the added benefit of minimizing retraining costs.

Keywords: Intelligent and autonomous space robotics systems, Planetary exploration
Abstract: Robotic information gathering methods combining Gaussian Process (GP) regression and Bayesian Optimization (BO) are effective for mapping of soil mechanical properties in uncertain or unknown extraterrestrial terrain. However, standard applications of such methods often model robot traversability as a simple go/no-go constraint, without fully incorporating it as a variable cost that impacts overall efficiency. This limitation is critical on rough terrains like the lunar surface, where traversability heavily influences the trade-off between survey time and modeling accuracy. To address this gap, we propose a comprehensive approach for slope-aware robotic information gathering. The core of this approach is a novel adaptive sampling method, Slope-aware Maximum Uncertainty Sampling (SaMUS), which features a new BO acquisition function that explicitly incorporates terrain slope to balance uncertainty reduction with path traversability. We demonstrate the effectiveness of our method through an extensive simulation study in two environment types: controlled basic terrains for qualitative analysis and realistic lunar terrains for evaluating the performance of the proposed method. The results show that the proposed approach generates significantly more time-efficient survey paths by strategically avoiding high-slope regions. Quantitative analysis on the realistic terrains further demonstrates that our method achieves a better trade-off between survey time and modeling accuracy, particularly on flat and steep terrains. Finally, we provide practical guidelines for selecting optimal parameters based on terrain class and operational time constraints.

Keywords: Planetary exploration, Intelligent and autonomous space robotics systems
Abstract: Planetary exploration robots must navigate uneven terrain while building reliable maps for space missions. However, most existing methods incorporate traversability constraints but may not handle high uncertainty in elevation estimates near complex features like craters, do not consider exploration strategies for uncertainty reduction, and typically fail to address how elevation uncertainty affects navigation safety and map quality. To address the problems, we propose a framework integrating safe path generation, adaptive confidence updates, and confidence-aware exploration strategies. Using Kalman-based elevation estimation, our approach generates terrain traversability and confidence scores, then incorporates them into Graph-Based exploration Planner (GBP) to prioritize exploration of traversable low-confidence regions. We evaluate our framework through simulated lunar experiments using a novel low-confidence region ratio metric, achieving 69% uncertainty reduction compared to baseline GBP. In terms of mission success rate, our method achieves 100% while baseline GBP achieves 0%, demonstrating improvements in exploration safety and map reliability.

Keywords: Multi-robot cooperation/collaboration, In-space manufacturing and assembly, Teleoperation and user interfaces
Abstract: This paper presents the software architecture and deployment strategy behind the MoonBot platform: a modular space robotic system composed of heterogeneous components distributed across multiple computers, networks and ultimately celestial bodies. We introduce a principled approach to distributed, heterogeneous modularity, extending modular robotics beyond physical reconfiguration to software, communication and orchestration. We detail the architecture of our system that integrates component-based design, a data-oriented communication model using ROS2 and Zenoh, and a deployment orchestrator capable of managing complex multi-module assemblies. These abstractions enable dynamic reconfiguration, decentralized control, and seamless collaboration between numerous operators and modules. At the heart of this system lies our open-source Motion Stack software, validated by months of field deployment with self-assembling robots, inter-robot cooperation, and remote operation. Our architecture tackles the significant hurdles of modular robotics by significantly reducing integration and maintenance overhead, while remaining scalable and robust. Although tested with space in mind, we propose generalizable patterns for designing robotic systems that must scale across time, hardware, teams and operational environments.

Keywords: Multi-robot cooperation/collaboration, Planetary exploration
Abstract: In this paper, we present the development of 4-DOF robot limbs, which we call Moonbots, designed to connect in various configurations with each other and wheel modules, enabling adaptation to different environments and tasks. These modular components are intended primarily for robotic systems in space exploration and construction on the Moon in our Moonshot project. Such modular robots add flexibility and versatility for space missions where resources are constrained. Each module is driven by a common actuator characterized by a high torque-to-speed ratio, supporting both precise control and dynamic motion when required. This unified actuator design simplifies development and maintenance across the different module types. The paper describes the hardware implementation, the mechanical design of the modules, and the overall software architecture used to control and coordinate them. Additionally, we evaluate the control performance of the actuator under various load conditions to characterize its suitability for modular robot applications. To demonstrate the adaptability of the system, we introduce nine functional configurations assembled from the same set of modules: 4DOF-limb, 8DOF-limb, vehicle, dragon, minimal, quadruped, cargo, cargo-minimal, and bike. These configurations reflect different locomotion strategies and task-specific behaviors, offering a practical foundation for further research in reconfigurable robotic systems.

Keywords: Multi-robot cooperation/collaboration, Intelligent and autonomous space robotics systems, Planetary exploration
Abstract: Exchanging information about the world among robots is fundamental for them to cooperate. However, challenges still persist when the robots exhibit heterogeneity in their hardware/software components, and different tasks such as observing and interacting with the environment are given to them. In this paper, we propose a new communication architecture and describe how it addressed these challenges with our team of heterogeneous robots developed for and demonstrated during the four-week Moon-analogue exploration mission on Mt. Etna, Italy. Our approach is to make each robot have its egocentric world model and exchange information only by remotely accessing the interfaces of other robots' world model. We offer key findings on the different communication strategies to trigger the remote interfaces as well as insights into how these information exchanges could compose a complex conversation. Through in-depth analyses of the real-world demonstration, we evaluate the contribution of such a communication design, and derive general design guidelines for information exchange about the world between robots in heterogeneous multi-robot systems.

Keywords: Planetary exploration, Multi-robot cooperation/collaboration, Intelligent and autonomous space robotics systems
Abstract: Planetary exploration is limited by slow hazard detection and constrained situational awareness when relying on ground-only platforms. This paper presents a cooperative framework integrating the Kalman Planetary Rover with a Companion UAV, demonstrating a synergistic air–ground system for analogue and future extraterrestrial missions. The Kalman Rover provides surface traversal, scientific payload handling, and autonomous navigation, while the UAV extends situational awareness, performs rapid reconnaissance, and relays environmental data. Communication is achieved via MAVLink over ExpressLRS integrated with the rover’s CAN bus, enabling near-real-time exchange of GPS waypoints, LiDAR maps, imagery, and telemetry. This architecture supports decentralized coordination, allowing the UAV to scout hazards and guide the rover’s path, improving efficiency and energy use. Future AI integration, including SLAM, sensor fusion, and autonomous task allocation, will enable dynamic cooperation and decision-making between platforms. Cooperative operations enhance exploration coverage, reduce human intervention, and improve scientific data quality through multi-perspective sensing. The proposed framework provides a foundation for scalable heterogeneous robotic teams capable of autonomous planetary exploration under limited communication and environmental constraints.

Keywords: Space logistics, Intelligent and autonomous space robotics systems, In-situ Resource Utilization
Abstract: This study presents the design, development, and evaluation of a microwave-based wireless power transmission (MWPT) system to enable efficient, cable-free power delivery to modular robotic systems on the lunar surface. Focusing on MoonBot—an autonomous, reconfigurable robot developed under Japan’s Moonshot R&D program, the research proposes a scalable rectenna system to receive microwave power and wirelessly charge MoonBots deployed for tasks such as lunar exploration and outpost construction. A 2.45 GHz rectenna prototype was developed using a hexagonal patch antenna and a Schottky-diode-based rectifier with 81.7% and 85% RF–DC conversion efficiency at 1 W and 1.3 W input powers respectively. Scaled multi-element configurations were tested to evaluate end-to-end system performance. Far-field simula- tions were extended to distances up to 1000 m, revealing that a 100-element rectenna can deliver 100 W of rectified DC power with a transmitted power of 15.17 MW. Furthermore, compact honeycomb layout arrangement of rectenna elements was proposed to enhance power capture and reduce required transmission power. Based on these results, estimated charging times for different MoonBot configurations were calculated. This research demonstrates a scalable and functional MWPT receiver prototype for the potential development of wireless power infrastructure for modular lunar robots.